{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from timeit import default_timer as timer\n",
    "# needed to see images\n",
    "from IPython.display import display, Image\n",
    "import pickle\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "# needed for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "current_palette = sns.color_palette()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getkey(item):\n",
    "    fpr, tpr, _ = roc_curve(test_labels.ravel(), test_preds[item])\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    return roc_auc\n",
    "\n",
    "def plotROC(testlabels, test_preds):\n",
    "    classifiers = list(test_preds.keys())\n",
    "\n",
    "    # Plot all ROC curves\n",
    "    plt.figure(figsize=(15,9))\n",
    "    for i, clf in zip(range(len(classifiers)), sorted(classifiers, key=getkey, reverse=True)):\n",
    "        fpr, tpr, _ = roc_curve(testlabels.ravel(), test_preds[clf] )\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        plt.plot(fpr, tpr,\n",
    "                 label='ROC curve '+ clf +  ' (area = {0:0.4f})'\n",
    "                       ''.format(roc_auc), linestyle='-', linewidth=2)\n",
    "\n",
    "\n",
    "    plt.plot([0, 1], [0, 1], 'k--', lw=2)\n",
    "    plt.xlim([-0.1, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Comparison of multiclass ROC curves')\n",
    "    plt.legend(loc=\"lower right\", fontsize=14)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "const_MFCC\n",
      "features193\n",
      "flat0pad\n",
      "const_LogMFCC\n"
     ]
    }
   ],
   "source": [
    "dataset_acc = pickle.load(open('dataset_acc.p','rb'))\n",
    "cnn = []\n",
    "rnn = []\n",
    "dnn = []\n",
    "for s in dataset_acc:\n",
    "    print(s)\n",
    "    cnn.append((s, dataset_acc[s]['CNN']))\n",
    "    rnn.append((s, dataset_acc[s]['RNN']))\n",
    "    dnn.append((s, dataset_acc[s]['DeepNN']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['58.2474226804', '63.45933562428408', '0.0', '39.34707903780069'], \n",
       "      dtype='<U17')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(cnn)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RGhoqXrsbN26I9fhv9pU35ZZyxcXF4fbt23jy5AkePnyIS5cuAYDKHPtffPEF1qxZgw4d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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14295c569e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[12,7])\n",
    "plt.title('Model Comparison across Datasets', fontsize=20)\n",
    "plt.plot(np.array(rnn)[:,1], 'o-', markersize=15, label='RNN')\n",
    "plt.plot(np.array(dnn)[:,1], 'o-', markersize=15, label='DeepNN')\n",
    "plt.plot(np.array(cnn)[:,1], 'o-', markersize=15, label='CNN')\n",
    "plt.xlabel('Dataset', fontsize=18)\n",
    "plt.xticks(range(4), np.array(cnn)[:,0], fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.ylabel('Accuracy (%)', fontsize=18)\n",
    "plt.ylim([0, 100])\n",
    "plt.xlim([-1, 4])\n",
    "plt.legend(loc=\"upper right\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "def runNN(data, truth): \n",
    "    nnet = OneVsRestClassifier(MLPClassifier(max_iter=5000, shuffle=True, hidden_layer_sizes=(500, 600)))\n",
    "    nnetmodel = nnet.fit(data, truth)\n",
    "    return nnetmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working dataframe's shape: (8730, 194)\n"
     ]
    }
   ],
   "source": [
    "data = pickle.load(open('193_features.p', 'rb'))\n",
    "s = list(data['sample'])\n",
    "s = pd.DataFrame(s)\n",
    "data_cols = s.columns\n",
    "s['label'] = data['label']\n",
    "print('working dataframe\\'s shape:', s.shape)\n",
    "test_preds = {}\n",
    "train = s[0:6984]\n",
    "test = s[6984:]\n",
    "LB = LabelBinarizer().fit(train['label'])\n",
    "test_labels = LB.transform(test['label'])\n",
    "del data, s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nn = runNN(train[data_cols], LB.transform(train['label']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done!\n"
     ]
    }
   ],
   "source": [
    "print('done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19358533791523483"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn.score(test[data_cols], test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
